Jabbary, A.; Pourmahmoud, N.; Abdollahi, M.A.A.; Rosen, M.A. Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells. International Journal of Green Energy 2023, 21, 1550–1566, doi:10.1080/15435075.2023.2262006.
Jabbary, A.; Pourmahmoud, N.; Abdollahi, M.A.A.; Rosen, M.A. Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells. International Journal of Green Energy 2023, 21, 1550–1566, doi:10.1080/15435075.2023.2262006.
Jabbary, A.; Pourmahmoud, N.; Abdollahi, M.A.A.; Rosen, M.A. Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells. International Journal of Green Energy 2023, 21, 1550–1566, doi:10.1080/15435075.2023.2262006.
Jabbary, A.; Pourmahmoud, N.; Abdollahi, M.A.A.; Rosen, M.A. Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells. International Journal of Green Energy 2023, 21, 1550–1566, doi:10.1080/15435075.2023.2262006.
Abstract
This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters, and consumption and output power were the target parameters of the multi-objective optimization algorithm. Then we used artificial intelligence techniques, including deep neural networks and polynomial regression, to model the data. Next, we employed the RSM (Response Surface Method) method to derive the target functions. Furthermore, we applied the NSGA-II multi-objective genetic algorithm to optimize the targets. Compared to the base model (Cubic), the optimized Pentagonal and Hexagonal models averagely increase the output current density by 21.819\% and 39.931\%, respectively.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
